Artificial intelligence assisted rule generation

ABSTRACT

A method improves a security of a computer system by building a new set of rules for the computer system. One or more processors input a plurality of client profiles to an artificial intelligence (AI) system, where the plurality of client profiles are based on an analysis of respective client environments comprising client assets and an intrusion detection alert history of a plurality of clients. The processor(s) match a new client profile to a respective client profile from the plurality of client profiles. The processor(s) build a new set of rules for the new client based on a similarity measure of the new client profile to the respective client profile. The processor(s) subsequently receive information indicating that a violation of the new set of rules has occurred and then execute a security feature of the computer system in order to resolve the violation of the new set of rules.

BACKGROUND

The present invention relates to the field of computer security, andspecifically to rule-based computer security. Still more particularly,the present invention relates to deploying rules to computer systems.

Computer security services are responsible for ingesting and correlatinglog data using custom rules, creating alerts and notifying clients ofpossible attacks. Such services are often provided from a single vendorto multi-thousand clients worldwide.

Thousands of actionable intelligence events (e.g., “alerts”) aregenerated daily by correlating multi-billions of log events from manythousands of data sources and devices. This enables the service todetect threats that are specific for certain computersystems/architectures. That is, such systems use custom SecurityInformation and Event Management (SIEM) rules that are specific to aparticular Information Technology (IT) environment (e.g., specifichardware, software, workloads, type of enterprise, etc.), and thus donot scale horizontally over other clients with similar but slightlydifferent IT profiles. As a result, alerts are not generated, tracked orremediated for clients with similar but slightly different IT domains.Such clients' computer systems remain oblivious and susceptible toattacks that are unknown in their environment because of a missingcorrelating rule.

SUMMARY

In an embodiment of the present invention, a method improves a securityof a computer system by building a new set of rules for the computersystem. One or more processors input a plurality of client profiles toan artificial intelligence (AI) system, where the plurality of clientprofiles are based on an analysis of respective client environmentscomprising client assets and an intrusion detection alert history of aplurality of clients. The processor(s) match a new client profile for anew client to a respective client profile from the plurality of clientprofiles, where the respective client profile is for a particular clientfrom the plurality of clients. The processor(s) build a new set of rulesfor the new client based on a similarity measure of the new clientprofile to the respective client profile. The processor(s) subsequentlyreceive information indicating that a violation of the new set of ruleshas occurred and, in response to the new set of rules being violated,execute a security feature of the computer system of the new client inorder to resolve the violation of the new set of rules.

In an embodiment of the present invention, a method improves a securityof a computer system by building a new rule based on a rule used byanother client. One or more processors input a plurality of clientprofiles to an artificial intelligence (AI) system, where the pluralityof client profiles are based on an analysis of respective clientenvironments comprising client assets and an intrusion detection alerthistory of a plurality of clients. The processor(s) match a new clientprofile for a new client to a respective client profile from theplurality of client profiles, where the respective client profile is fora particular client from the plurality of clients. The processor(s) thenbuild a new rule for the new client based on a rule used by theparticular client. The processor(s) subsequently receive informationindicating that a violation of the new rule has occurred. In response tothe new rule being violated, the processor(s) execute a security featureof the computer system of the new client in order to resolve theviolation of the new rule.

In an embodiment of the present invention, a method improves a securityof a computer system used by a first (new) client by assigning a rulefrom a second (particular) client to the first client for use by thecomputer system used by the first client. One or more processors input aplurality of client profiles to an artificial intelligence (AI) system,where the plurality of client profiles are based on an analysis ofrespective client environments comprising client assets and an intrusiondetection alert history of a plurality of clients. The processor(s)match a new client profile for the new client to a respective clientprofile from the plurality of client profiles, where the respectiveclient profile is for the particular client from the plurality ofclients. The processor(s) assign a particular rule from the particularclient to the new client based on the new client profile matching therespective client profile. The processor(s) subsequently receiveinformation indicating that a violation of the particular rule hasoccurred, and, in response to the particular rule being violated,execute a security feature of the computer system of the new client inorder to resolve the violation of the particular rule.

In one or more embodiments, the method(s) described herein are performedby an execution of a computer program product and/or a computer system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an exemplary system and network in which the presentinvention may be implemented;

FIG. 2 depicts an overall solution architecture used by one or moreembodiments of the present invention;

FIG. 3 illustrates an exemplary advanced threat disposition system usedin one or more embodiments of the present invention;

FIG. 4 depicts an exemplary Recurrent Neural Network (RNN) as used inone or more embodiments of the present invention;

FIG. 5 illustrates an exemplary profile correlator as used in one ormore embodiments of the present invention;

FIG. 6 depicts an exemplary advanced rule analyzer as used in one ormore embodiments of the present invention;

FIG. 7 illustrates an exemplary rule artificial intelligence (AI) systemas used in one or more embodiments of the present invention;

FIG. 8 depicts an exemplary master AI system as used in one or moreembodiments of the present invention;

FIG. 9 is a high-level flow chart of one or more steps performed inaccordance with one or more embodiments of the present invention;

FIG. 10 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 11 depicts abstraction model layers of a cloud computer environmentaccording to an embodiment of the present invention.

DETAILED DESCRIPTION

In one or more embodiments, the present invention is a system, a method,and/or a computer program product at any possible technical detail levelof integration. In one or more embodiments, the computer program productincludes a computer readable storage medium (or media) having computerreadable program instructions thereon for causing a processor to carryout aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

In one or more embodiments, computer readable program instructions forcarrying out operations of the present invention comprise assemblerinstructions, instruction-set-architecture (ISA) instructions, machineinstructions, machine dependent instructions, microcode, firmwareinstructions, state-setting data, or either source code or object codewritten in any combination of one or more programming languages,including an object oriented programming language such as Java,Smalltalk, C++ or the like, and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. In one or more embodiments, the computer readable programinstructions execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario and in one or moreembodiments, the remote computer connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection is made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider). In some embodiments, electronic circuitry including, forexample, programmable logic circuitry, field-programmable gate arrays(FPGA), or programmable logic arrays (PLA) execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

In one or more embodiments, these computer readable program instructionsare provided to a processor of a general-purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. In one or moreembodiments, these computer readable program instructions are also bestored in a computer readable storage medium that, in one or moreembodiments, direct a computer, a programmable data processingapparatus, and/or other devices to function in a particular manner, suchthat the computer readable storage medium having instructions storedtherein comprises an article of manufacture including instructions whichimplement aspects of the function/act specified in the flowchart and/orblock diagram block or blocks.

In one or more embodiments, the computer readable program instructionsare also be loaded onto a computer, other programmable data processingapparatus, or other device to cause a series of operational steps to beperformed on the computer, other programmable apparatus or other deviceto produce a computer implemented process, such that the instructionswhich execute on the computer, other programmable apparatus, or otherdevice implement the functions/acts specified in the flowchart and/orblock diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams represents a module, segment, or portion ofinstructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block occur out of the ordernoted in the figures. For example, two blocks shown in succession are,in fact, executed substantially concurrently, or the blocks aresometimes executed in the reverse order, depending upon thefunctionality involved. It will also be noted that, in one or moreembodiments of the present invention, each block of the block diagramsand/or flowchart illustration, and combinations of blocks in the blockdiagrams and/or flowchart illustration, are implemented by specialpurpose hardware-based systems that perform the specified functions oracts or carry out combinations of special purpose hardware and computerinstructions.

With reference now to the figures, and in particular to FIG. 1, there isdepicted a block diagram of an exemplary system and network that may beutilized by and/or in the implementation of the present invention. Notethat some or all of the exemplary architecture, including both depictedhardware and software, shown for and within computer 102 may be utilizedby software deploying server 150 and/or telemetry source 152 and/orclient computers 154 and/or intrusion detection system 156.

Exemplary computer 102 includes a processor 104 that is coupled to asystem bus 106. Processor 104 may utilize one or more processors, eachof which has one or more processor cores. A video adapter 108, whichdrives/supports a display 110, is also coupled to system bus 106. Systembus 106 is coupled via a bus bridge 112 to an input/output (I/O) bus114. An I/O interface 116 is coupled to I/O bus 114. I/O interface 116affords communication with various I/O devices, including a keyboard118, a mouse 120, a media tray 122 (which may include storage devicessuch as CD-ROM drives, multi-media interfaces, etc.), a recurrent neuralnetwork 124 (described in greater detail in an exemplary embodimentdepicted in FIG. 4), and external USB port(s) 126. While the format ofthe ports connected to I/O interface 116 may be any known to thoseskilled in the art of computer architecture, in one embodiment some orall of these ports are universal serial bus (USB) ports.

As depicted, computer 102 is able to communicate with a softwaredeploying server 150, a telemetry source 152, and/or client computers154 using a network interface 130. Network interface 130 is a hardwarenetwork interface, such as a network interface card (NIC), etc. Network128 may be an external network such as the Internet, or an internalnetwork such as an Ethernet or a virtual private network (VPN).

A hard drive interface 132 is also coupled to system bus 106. Hard driveinterface 132 interfaces with a hard drive 134. In one embodiment, harddrive 134 populates a system memory 136, which is also coupled to systembus 106. System memory is defined as a lowest level of volatile memoryin computer 102. This volatile memory includes additional higher levelsof volatile memory (not shown), including, but not limited to, cachememory, registers and buffers. Data that populates system memory 136includes computer 102's operating system (OS) 138 and applicationprograms 144.

OS 138 includes a shell 140, for providing transparent user access toresources such as application programs 144. Generally, shell 140 is aprogram that provides an interpreter and an interface between the userand the operating system. More specifically, shell 140 executes commandsthat are entered into a command line user interface or from a file.Thus, shell 140, also called a command processor, is generally thehighest level of the operating system software hierarchy and serves as acommand interpreter. The shell provides a system prompt, interpretscommands entered by keyboard, mouse, or other user input media, andsends the interpreted command(s) to the appropriate lower levels of theoperating system (e.g., a kernel 142) for processing. Note that whileshell 140 is a text-based, line-oriented user interface, the presentinvention will equally well support other user interface modes, such asgraphical, voice, gestural, etc.

As depicted, OS 138 also includes kernel 142, which includes lowerlevels of functionality for OS 138, including providing essentialservices required by other parts of OS 138 and application programs 144,including memory management, process and task management, diskmanagement, and mouse and keyboard management.

Application programs 144 include a renderer, shown in exemplary manneras a browser 146. Browser 146 includes program modules and instructionsenabling a world wide web (WWW) client (i.e., computer 102) to send andreceive network messages to the Internet using hypertext transferprotocol (HTTP) messaging, thus enabling communication with softwaredeploying server 150 and other computer systems.

Application programs 144 in computer 102's system memory (as well assoftware deploying server 150's system memory) also include a ComputerSecurity Management Logic (CSML) 148. CSML 148 includes code forimplementing the processes described below, including those described inFIGS. 2-9. In one embodiment, computer 102 is able to download CSML 148from software deploying server 150, including in an on-demand basis,wherein the code in CSML 148 is not downloaded until needed forexecution. Note further that, in one embodiment of the presentinvention, software deploying server 150 performs all of the functionsassociated with the present invention (including execution of CSML 148),thus freeing computer 102 from having to use its own internal computingresources to execute CSML 148.

Also coupled to computer 102 is a telemetry source 152, which is asource of information regarding a security event, and is described infurther detail in telemetry source 252 in FIG. 2.

Client computers 154 are used by clients, such as the clients shown intable 204 in FIG. 2.

The client computers 154 are protected by an intrusion detection system156, which utilizes one or more of the rule-based features describedherein for detecting an intrusion on the client computers 154.

Note that the hardware elements depicted in computer 102 are notintended to be exhaustive, but rather are representative to highlightessential components required by the present invention. For instance,computer 102 may include alternate memory storage devices such asmagnetic cassettes, digital versatile disks (DVDs), Bernoullicartridges, and the like. These and other variations are intended to bewithin the spirit and scope of the present invention.

The present invention is described herein as providing a needed securityrule to a computer system. However, the method and system describedherein is not necessarily limited to security systems. Rather, themethod and system described herein is applicable to any data analyticsplatform that formulates processing dispositions on machine learningmodels.

With regard to the issue of a security system lacking a particular rulefor its architecture, one or more embodiments of the present inventionpresent a new and novel solution architecture to mitigate a gap in rulesfor a particular client. That is, a particular client system may bemissing a rule for responding to a security attack. One or moreembodiments of the present invention present an Artificial Intelligence(AI) assisted rule generation and actionable intelligence architecture.A security intrusion detection system takes alert disposition inputsfrom a threat analysis and disposition system, matches profiles from anasset profiler system (also referred to herein as a “profilecorrelator”) or source systems, and correlates (using a correlationengine) a rule design from a natural language processing (NLP) ruleanalytics system. In this architecture/mechanism, the correlation engineis independent of product categories and can be generically appliedacross different data domains. That is, in one or more embodiments, thepresent invention takes a financial rule developed from financialtelemetry analytics and derives a marketing rule from the financial rulebased on marketing data analytics. However, in one or more embodiments,each of these systems (i.e., the financial system and the marketingsystem) consume common telemetry, apply rules to process data, deriveactionable intelligence, and perform certain actions with theintelligence based on a violation of the rules.

By virtue of having visibility over multiple client environments, asystem (e.g., a supervisory system such as computer 102 shown in FIG. 1)is able to assess the effectiveness of actionable intelligence andsubsequent successful disposition of the alert for multiple clients.This provides a comparative analysis to other clients and theirenvironments in which there is a gap of coverage or generation ofactionable intelligence. The system thus is able to compare betweenprofiles and highlight gaps, which could be due to lack of data sources,rules or intel sources. The system is able to automatically notify theclient to add missing critical data sources, detection rules, intelfeeds, or any other domain level information that makes the system moreeffective. In one or more embodiments, this process is leveraged at thepre-boarding stage (i.e., before the client's computer system goes online to handle the information technology (IT) needs of the client)where an assessment can be made to identify gaps and recommend missingdomain information. In one or more embodiments, the process describedherein is also leveraged through the lifecycle of the system in order tomeasure the effectiveness or ineffectiveness of telemetry beingcollected, whereby if the collected telemetry is ineffective it can betuned accordingly or recommended to be disconnected.

Thus, in one or more embodiments of the present invention, the systemdynamically inserts rules that operate temporarily during a particularoccurrence of an event. That is, the inserted rule causes the clientcomputer's security system to monitor for zero day threats (e.g., towatch for a malware attack that is happening in a particular region). Arule is thus activated to respond to an attack that is detected based oncertain telemetry, and then the rule is deactivated after the attackevent. Learning how the disposition (attack and response) occurs andusing the domain information about the system that was attacked triggersthe cognitive system to build custom rules for each affected client inreal time, to notify the system of the changes, and to simultaneouslygenerate actionable intelligence on the notification ticketing system.

With reference now to FIG. 2, an overall solution architecture used byone or more embodiments of the present invention is presented.

The architecture 202 shown in FIG. 2 addresses the following problem.Assume that an actionable intelligence is detected by a rule for clientC1. For example, rule R2 may state “If three messages from an untrustedinterne protocol (IP) address are received by email server made byManufacturer X within ten seconds by the computer system of client C1,and if the untrusted IP address is on a “blacklist” of IP addresses thatclient C1 does not trust, and if the three messages all contain the word“urgent”, then direct the notification ticketing system associated withthe security system for client C1 to issue a ticket stating that clientC1 is likely under a security attack”. However, the actionableintelligence detected by rule R2 remains local to the environment forclient C1. That is, rule R2 is specific to emails that are received byan email server that is manufactured by Manufacturer X. As such, thedetected security incident detected by a Security Information and EventManagement (SIEM) rule is local to (i.e., tailored to) client C1 wherethe rule R2 exists.

However, assume now that client C2 shown in table 204 does not have anemail server that is manufactured by Manufacturer X. Rather, client C2uses an email server that is manufactured by Manufacturer Y. Therefore,client C2 does not have a copy of rule R2 (since R2 is specific forcomputer systems that use email servers built by Manufacturer X), eventhough Client C1 and C2 may have similar types of operations, and eventhough the email servers built by Manufacturer X perform the samefunctions as email servers built by Manufacturer Y.

As such, the process shown in FIG. 2 matches clients (e.g., client C1and client C2) that have a similar domain profile (i.e., similar typesof operations, similar types of equipment, etc.). The process shown inFIG. 2 then automatically generates and applies rules and intelligenceacross all similar clients.

The architecture 202 comprises the following system components.

Advanced Threat Disposition Scoring (ATDS) machine learning system 206is a machine learning based threat detection system. That is, ATDSmachine learning system 206 determines whether the computer system(s) ofa particular client (e.g., client C1) are under a security attack.Additional detail of ATDS machine learning system 206 are presented inFIG. 3.

Profile correlator 208 is a Natural Language Processing (NLP) basedsystem to match clients with similar profiles. Additional details ofprofile correlator 208 are presented in FIG. 5.

Rule analytics 210 is an NLP based Rule analytics system to decomposerules into sub components. Additional details of rule analytics 210 ispresented in FIG. 6.

Rule Artificial Intelligence (AI) machine learning system 212 is asupervised machine learning based system that is used to predict rulethresholds. Additional details of rule AI machine learning system 212 ispresented in FIG. 7.

Master AI System 214 is a solution aggregator, rule generator andoffense generator. Additional details of master AI system 214 ispresented in FIG. 8.

As shown in FIG. 2, master AI system 214 is architected to 1) predictactionable intelligence for a particular client (e.g., client C2); 2)add a new rule (e.g., rule R2); and 3) provide a customer notification(e.g., to client C2) that 1) a new rule has been added for client C2and/or that a security intrusion event (based on newly-added rule R2)has occurred. For purposes of illustration, rule R2 is used as anexample of a rule that is being violated and/or replicated. However, itis to be understood that the processes described herein are applicableto any rule that is being violated and/or replicated, etc.

With reference now to exemplary table 204, client C2 does not have arule R2, even though clients C1, C3, and C4 have a rule R2. As describedabove, rule R2 essentially states that if certain events occur (e.g.,suspicious readings from sensors, messages from blacklisted IPaddresses, etc.), then a security breach occurrence is determined to beoccurring.

In an embodiment of the present invention, the events related to thevarious rules depicted (for clients C1, C3, and C4) are identical. Thatis, every condition/event is identical, including which specificequipment is involved, what type of enterprise activities are involved,which specific messages are involved, etc. If master AI system 214determines that C2 has the specific equipment described in rule R2, andhas the same type of enterprise activity (e.g., banking) as clients C1,C3, and C4, then master AI system 214 will directly assign rule R2 toclient C2.

However, in another embodiment of the present invention, the eventsrelated to the various rules depicted (for clients C1, C3, and C4) arenot identical. For example, assume again that rule R2 depends on whichspecific equipment is involved and what type of enterprise activitiesare involved. Assume further that master AI system 214 determines thatclient C2 has the same type of enterprise activity (e.g., banking) asclients C1, C3, and C4, but that client C2 does not use the samespecific equipment as clients C1, C3, and C4. For example, assume thatclients C1, C3, and C4 use an email server that is manufactured byCompany X, while client C2 uses an email server that is manufactured byCompany Y. Assume further, however, that the email server that ismanufactured by Company X performs the same function as the email serverthat is manufactured by Company Y, although the two email servers mayhave different features, security levels, etc. Nonetheless, in thisembodiment the master AI system 214 will create a version of rule R2(e.g., rule R2′) that is functionally the same as rule R2, even thoughrule R2′ is designed to work with the email server that is manufacturedby Company Y while rule R2 was designed to work with the email serverthat is manufactured by Company X.

Referring again to table 204, assume that initially client C2 does nothave rule R2′. However, ATDS machine learning 206 has determined throughclients C1, C3, and/or C4 that rule R2 has been violated/triggered, thusindicating that a security issue (e.g., a viral attack, a dedicateddenial of service attack, etc.) has arisen within their system(s). Forexample, if rule R2 is violated in the computer system for client C1,then an occurrence (e.g., a viral attack, a dedicated denial of serviceattack, etc.) is deemed to be occurring in the computer system forclient C1, as shown by “R2>O1”. Similarly, if rule R2 is violated in thecomputer system for client C3, then an occurrence is deemed to beoccurring in the computer system for client C3, as shown by “R2>O3”.Similarly, if rule R2 is violated in the computer system for client C4,then an occurrence is deemed to be occurring in the computer system forclient C3, as shown by “R2>O4”.

Thus, ATDS machine learning system 206 determines that rule R2 has beenviolated in one or more of the clients C1, C3, and C4, and uses thisinformation for the purposes. Later, when ATDS machine learning system206 also tracks rules violations for client C2, the following actionsare also performed for client C2.

First, ATDS machine learning system 206 uses the determination that ruleR2 has been violated as the basis for generating an escalation message216, which is sent to the security systems 218 for clients C1, C3, andC4. These security systems 218 are security management personnel in anembodiment of the present invention. However, in a preferred embodimentof the present invention, security systems 218 are automated securitysystems that turn off certain devices, block messages from certain IPaddresses, upgrade firewalls, etc.

For example, assume that one of the automated security systems 218assigned to client C1 is associated with a supervisory control and dataacquisition (SCADA) system that controls pumps in a refinery. Assumefurther that the escalation message 216 indicates that a message hasbeen received that 1) instructs a critical pump to turn off, and that 2)the message is from an untrusted source. As such, the automated securitysystem 218 for client C1 will automatically direct the SCADA system tokeep the critical pump turned on, and/or to properly shut down an entireunit that uses that critical pump until the issue is resolved.

In another embodiment, if rule R2 is violated, then messages fromcertain IP addresses, as defined by rule R2, are blocked.

Second, ATDS machine learning system 206 uses the determination thatrule R2 has been violated to update the profile correlator 208. That is,the details of the violation of the rule R2 by one or more of clientsC1, C3, and C4 is sent to the profile correlator 208, which determinesthe overall effect of the violation of the rule R2, particularly as itaffects one or more assets (e.g., equipment, computers, data storage,software, etc.) of the affected client from clients C1, C3, and C4. Thisupdated information is then sent to a customer database 220, whichincludes customer asset profiles for clients C1, C3, and C4.

Third, ATDS machine learning system 206 uses the determination that ruleR2 has been violated to tell the rules analytics 210 that the violationof rule R2 has occurred. This allows the rules analytics 210 to evaluatethe rule and the violation, in order to update rule R2. For example,assume that rule R2 is violated based on an email being received from anuntrusted (blacklisted) IP address. However, rules analytics 210, usingrule test conditions from a set of security information and eventmanagement (SIEM) rule 222, will modify rule R2 such that any messagethat has similar wording and/or actions (e.g., accessing a certaindatabase) will also be prevented by the firewall from being received bythe computer system, even if the similar message came from a trusted IPaddress.

Fourth, ATDS machine learning system 206 uses the determination thatrule R2 has been violated to let the master AI system 214 know what ishappening in the clients C1, C3, and C4 with regard to rule R2.

As shown in the master AI system 214, the master AI system 214 now hasmultiple sources of information to use when assigning a new rule R2(e.g., rule R2′) to client C2.

That is, inputs to the master AI system 214 include 1) the informationfrom the ATDS machine learning system 206 letting it know how and ifrule R2 has been violated; 2) the output from the rules analytics 210describing what modifications, if any, to the rule R2 have occurred; 3)the output of the rule AI machine learning system 212 that describespredicted thresholds and boundaries that must be met for rule R2 to beviolated, based on rule conditions, event conditions, and behaviorconditions set by the SIEM rules 222; and 4) the output from the profilecorrelator 208 that describe the profile of any client that is affectedby the violation of rule R2.

In addition, the master AI system 214 receives inputs from telemetrysources 252, log sources 226, and domain intelligence mapping 228.

Telemetry sources 252 (analogous to telemetry source 152 shown inFIG. 1) are any source of information regarding an event. For example,in one embodiment, a telemetry source 252 is a sensor that detects thata processor is being overused, resulting in a slowdown of an entirecomputer system. In another embodiment, a telemetry source 252 is asensor that detects that an email server has received an email from anuntrusted IP address. In another embodiment, a telemetry source 252 is asocial media platform, which has posted a message related to rule R2such as “I'm getting bombarded with emails from untrusted IP addressx.x.x.x. Watch out!”

Log sources 226 contain logs of events, including logs of sensors withina computer system, messages posted on a social media service, etc.

Domain intelligence mapping 228 searches a large source of data (e.g.,the World Wide Web) looking for certain key words, patterns, etc., thatare indicative of events that will violate rule R2.

Thus, in one or more embodiments of the present invention, master AIsystem 214 utilizes the various inputs shown in FIG. 2 to determine thatclient C2 needs to have a copy of rule R2 (or at least a variation ofrule R2 such as rule R2′) as part of its security infrastructure.

With reference now to FIG. 3, an exemplary advanced threat dispositionscoring (ATDS) system 306 (analogous to ATDS machine learning system 206shown in FIG. 2) is presented.

As shown in FIG. 3, ATDS system 306 uses machine learning in order todetermine whether an offense (e.g., a security attack on a computersystem) should be addressed (e.g., escalated to the generation of aticket/report/action for the offense) or ignored (closed). The decisionas to whether the offense should be addressed or ignored is based on amachine learning process determining the likelihood that the offense issignificant enough to warrant further actions. That is, the ATDS system305 predicts what the disposition of the offense should be. In one ormore embodiments of the present invention, this prediction/decision isbased on how confident the AI process is that the offense warrantsfurther action.

As shown in FIG. 3, various types of machine learning processes are usedin various embodiments of the present invention. That is, differentembodiments may use one, two or all three of the machine learningprocesses depicted as machine learning (ML) model 1 depicted in block303, ML model 2 (depicted in block 305) and/or ML model 3 (depicted inblock 324) when determining whether an offense 301 should be addressedor ignored.

Block 303 represents a gradient boosting machine (GBM) machine learningprocess, which uses multiple decision trees that utilize each other'sanalysis, thus “boosting” the process in order to learn. That is, assumethat first decision tree is a “weak learner” that has many errors whenmaking a prediction based on a set of input data. These errors are thenweighted such that they are heavily used to retrain a second decisiontree. The process continues until the final model/decision tree iseffective at properly predicting a correct output based on any inputdata.

Block 305 represents a random forest machine learning process, whichalso uses decision trees, but randomly combines decision trees into a“random forest” of trees. This allows the system to bag features indifferent decision trees such that features in a particular limb/node invarious decision trees that are very strong predictors thus describe thedifferent decision trees as be correlated. That is, a particular featurethat turns out to be a good predictor of some outcome in differentdecision trees makes these different decision trees correlated, sincethey produce the same accurate prediction from the same feature.

Block 324 represents a deep learning machine learning model. Anexemplary deep learning machine learning model as used by one or moreembodiments of the present invention is a recurrent neural network, asshown in FIG. 4.

With reference now to FIG. 4, an exemplary recurrent neural network(RNN) 424 (analogous to RNN 124 shown in FIG. 1) is presented. In anRNN, neurons are arranged in layers, known as an input layer 403, hiddenlayers 405, and an output layer 407. The input layer 403 includesneurons/nodes that take input data, and send it to a series of hiddenlayers of neurons (e.g., hidden layers 405), in which all neurons fromone layer in the hidden layers are interconnected with all neurons in anext layer in the hidden layers 405. The final layer in the hiddenlayers 405 then outputs a computational result to the output layer 407,which is often a single node for holding vector information.

As just mentioned, each node in the depicted RNN 424 represents anelectronic neuron, such as the depicted neuron 409. As shown in block411, each neuron (including neuron 409) functionally includes at leastthree features: an algorithm, an output value, and a weight.

The algorithm is a mathematic formula for processing data from one ormore upstream neurons. For example, assume that one or more of theneurons depicted in the middle hidden layers 405 send data values toneuron 409. Neuron 409 then processes these data values by executing thealgorithm shown in block 411, in order to create one or more outputvalues, which are then sent to another neuron, such as another neuronwithin the hidden layers 405 or a neuron in the output layer 407. Eachneuron also has a weight, that is specific for that neuron and/or forother connected neurons.

For example, assume that neuron 413 is sending the results of itsanalysis of a piece of data to neuron 409. Neuron 409 has a first weightthat defines how important data coming specifically from neuron 413 is.If the data is important, then data coming from neuron 413 is weightedheavily, thus causing the algorithm(s) within neuron 409 to generate ahigher output, which will have a heavier impact on neurons in the outputlayer 407. Similarly, if neuron 413 has been determined to besignificant to the operations of neuron 409, then the weight in neuron413 will be increased, such that neuron 409 receives a higher value forthe output of the algorithm in the neuron 413. These weights areadjustable for one, more, or all of the neurons in the RNN 424, suchthat a reliable output will result from output layer 407. Suchadjustments may be performed manually or automatically.

When manually adjusted, the weights are adjusted by the user, sensorlogic, etc. in a repeated manner until the output from output layer 407matches expectations. For example, assume that input layer 403 receivescertain values of data represented by offense 301 shown in FIG. 3. Ifthe output from output layer 407 is a vector that fails to accuratelydescribe a known security attack, then the weights (and alternativelythe algorithms) of one or more of the neurons in the RNN 424 areadjusted until the vector generated by output layer 407 has a value thatis associated with the known security attack (or the prediction of aknown security attack).

When automatically adjusted, the weights (and/or algorithms) areadjusted using “back propagation”, in which weight values of the neuronsare adjusted by using a “gradient descent” method that determines whichdirection each weight value should be adjusted to. This gradient descentprocess moves the weight in each neuron in a certain direction until theoutput from output layer 407 improves (e.g., gets closer to representinga certain security attack and/or predicting a certain security attack).

Other types of machine learning processes/algorithms used in variousembodiments include a support vector machine (that causes data to betrained to align in a linear vector), linear regression (which models arelationship between a scalar and one or more independent variables),logistic regression (which is applied to binary dependent variables),etc.

Returning to FIG. 3, the decision model 307 is thus able to decide(based on the output of one or more of the ML Models 1-3 depicted inblocks 303, 305, and/or 324) whether the recognized offense should beclosed (block 309), and thus marked as being closed in a database 320;or whether the recognized offense should be escalated (block 317). Ifthe offense is escalated, then a notification/ticket is sent to theclient 318 (analogous to automated security system 218 shown in FIG. 2);a profile correlator 308 (analogous to profile correlator 208 shown inFIG. 2); a master AI engine 314 (analogous to master AI system 214 shownin FIG. 2); and/or a rules analytics engine 310 (analogous to ruleanalytics 210 shown in FIG. 2).

Thus, the ATDS system 306 includes one or more (preferably at leastthree as depicted in FIG. 3) separate algorithms that provide an offensedisposition classification (escalated versus closed) on each incomingalert with a confidence threshold. Thus, the decision model 307 closesor auto escalates the offense based on a set threshold and decisionlogic, such that escalated alerts are forwarded to profile correlator308, rule analytics engine 310, and master AI engine 314, as well as theclient(s) 318.

FIG. 5 illustrates an exemplary profile correlator as used in one ormore embodiments of the present invention.

As depicted in FIG. 5, profile correlator 508 (analogous to profilecorrelator 208 shown in FIG. 2) takes offense vector input from ATDS 506(analogous to ATDS machine learning system 206) and profile inputs fromcustomer asset profile database 520 (analogous to customer asset profiledatabase 220 shown in FIG. 2). The client profiles are then cleaned,transformed and converted to strings.

For example, the client profile 503 for Client A includes string datathat describes where the client is located (Gi, Gj); what type ofindustry that client is working in (Ij); a description of the type of ITequipment that client uses (Ti, Tm, Tn); what types of log sources (Li,Lp, Lq that client uses; what types of security systems are used by thatclient to protect his IT system (Sp, St, Sf, etc.); and what criticalbusiness applications are used by that client (Ai, Ap, Al, etc.). Thisinformation is then tokenized (i.e., sensitive data is replaced withunique identification symbols that retain necessary information aboutthe data without revealing any sensitive information about the data) andvectorized (and/or weighted) using an algorithm such as a termfrequency-inverse document frequency (TF-IDF) algorithm that determineshow important a particular type of client profile information is indetermining whether or not to escalate or abort an offense, as shown inblock 505. That is, the TF-IDF algorithm identifies certain types ofprofile information as being critical to this determination based ontheir frequency of occurrence in offense evaluation algorithms.

Profile correlator 508 uses Natural Language Processing (NLP) methods501 to identify a similarity between clients (e.g., Client A, Client B,etc.) having similar information technology (IT) profiles.

A similarity score is calculated for each set of customers' string datausing cosine similarity algorithm (see block 507). Clients with asimilarity score above a specified threshold (x %) are filtered andoutputted to a master AI engine, such as the depicted AI master 514(analogous to AI master system 214 shown in FIG. 2).

FIG. 6 depicts a rules analytics engine 610 (analogous to rule analytics210 shown in FIG. 2) as an exemplary advanced rule analyzer used in oneor more embodiments of the present invention.

Inputs from ATDS 606 (analogous to ATDS machine learning system 206shown in FIG. 2) and a client SIEM 622 (analogous to SIEM rules 222shown in FIG. 2) are input to the rule analytics engine 610. Anexemplary input from the ATDS 606 is a rule name for one of the rulesdescribed in table 204 in FIG. 2. An exemplary input from the SIEM 622is an extensible markup language (XML) file that describes and/orimplements the named rule found in a tool or rule library within theSIEM 622.

A parse rule logic 602 parses out the received rules into a tidy format(e.g., a tabular format) and NLP string methods are applied fortransformation of the rules. That is, terms in the rules are parsed bythe NLP methods 601 (analogous to NLP methods 501 shown in FIG. 5) intovarious words/phrases, which are then graphically described accordingtheir proximate, contextual, and frequency relationship to one another.This parsing/transformation by the parse rule logic 602 leads to adescription of test conditions 604 that are to be used when testing therule (e.g., what type of testing algorithm and/or machine learningsystem should be used, what type of hardware should be used, etc.).

The parsing/transformation of the rule by the parse rule logic 602 alsoleads to a description of which log sources (e.g., source of telemetrydata, social media comments, etc.) should be used to test the rules, asshown in block 606.

The parsing/transformation of the rule by the parse rule logic 602 alsoleads to a description of rule thresholds that should be used whentesting the rules, as described in block 608. For example, a rule maystate that if 90% of incoming emails are from unknown IP addresses, thena ticket should be issued. In this example, “90%” is the threshold ofthe rule that needs to be reached in order to issue a ticket.

The parsing/transformation of the rule by the parse rule logic 602 alsoleads to a descriptor of operators 612 that are to be used when testingand/or using the rule. For example, an operator such as a mapping ofterms of the rule and/or inputs to the rule will assign and describesuch terms/inputs in a logical tree, vector space, etc.

Thus, the test conditions 604, log source types (block 606), thresholds(block 608), and operators 612 are extracted out of the rules by theparse rule logic 602.

Furthermore, client specific information (e.g., name, account number,type of industry, etc.) is stripped out of the rules, as shown in block614.

As shown in block 616, the rule is then decomposed into individualvector components (e.g., predicate conditions, described in a rule, thatare necessary to escalate an offense) that can be assembled in a ruletemplate.

The vectorized rule template is outputted to a Master AI engine depictedin FIG. 6 as master AI 614 (analogous to AI master system 214 shown inFIG. 2).

With reference now to FIG. 7, an exemplary rule artificial intelligence(AI) system as used in one or more embodiments of the present inventionis presented.

As shown in FIG. 7, a rule AI system 712 (analogous to rule AI machinelearning system 212 shown in FIG. 2) receives inputs from threatintelligence feeds 701, SIEM 722 (analogous to SIEM rules 222 shown inFIG. 2), and security solutions 703.

The threat intelligence feeds 701 include feeds from telemetry sources252, log source 226, and domain intelligence mapping 228 shown in FIG.2.

Security solutions 703 are solutions to security breaches that have beenpre-established, such as raising a firewall to a higher level of trafficrestriction, turning off certain IT devices, etc.

As shown in FIG. 7, flow conditions 705, event conditions 707, offenseconditions 709, behavior conditions 711, and miscellaneous ruleconditions 713 are parsed out to build features needed for a rule (block715).

Flow conditions 705 describe an order in which certain events must occurin order to trigger a rule. For example, events E1, E2, and E3 mustoccur in that order in order to trigger Rule R1. If these events occurin the order E1, E3, and E2, then Rule R1 will not be triggered.

Event conditions 707 describe the events that must occur in order to fora rule to be triggered. For example, an exemplary event E1 could bereceipt of an email from a blacklisted IP, an exemplary event E2 couldbe a power surge in the computer that received the email, and exemplaryevent E3 could be a shut-down of the computer.

Offense conditions 709 describe those events that must occur in order totrigger the offense, as well as their order, timing, etc.

Behavior conditions 711 describe how the computer must behave (e.g.,processing throughput, available bandwidth, etc.) in order to triggerthe offence. For example, even after the events occur, the computer mustbehave in a certain way, such as activating a web browser, even if therule does not prohibit this.

Miscellaneous rule conditions 713 are any user-defined conditions thatare to be considered when the system creates a particular rule.

Once the new rule is initially generated by the rule AI system 712 (seeblock 715), the new rule is used to train a machine learning model 717.For example, in an embodiment of the present invention, a neural networksuch as RNN 424 shown in FIG. 4 is built to emulate the newly createdrule. The RNN 424 is adjusted (e.g., by adjusting the algorithms, outputvalues, weights within one or more of the neurons) until triggeringevents, which fed into the RNN 424, result in an output from the RNN 424indicating that an offense to the security of a computer system hasoccurred.

In an embodiment of the present invention, the machine learning model717 is a supervised machine learning classification model that usesalgorithms such as Support Vector Machines (SVM).

In an embodiment of the present invention, the supervised machinelearning based system that is the rule AI system 712 takes input testconditions from rule libraries/SIEM tools, security vendor rules, etc.using various learning models for different threshold types (frequency,count, time).

As shown in block 719, rule thresholds are predicted and sent to the AImaster 714 (analogous to AI master system 214 shown in FIG. 2). That is,once the rule AI system 712 parses test conditions and engineerfeatures, rule thresholds (i.e., what thresholds must be exceeded in theconditions of the rule) are set and then labeled, in order to train deeplearning systems.

With reference now to FIG. 8, an exemplary master AI 814 (analogous toAI master system 214 shown in FIG. 2) as used in one or more embodimentsof the present invention is presented.

Master AI 814 is the final solution integrator that integrates outputsfrom all of the system components and generates custom rules for clientsmatched by the profile correlator.

Master AI 814 takes inputs from a profile correlator 808 (analogous toprofile correlator 208 shown in FIG. 2), a rule AI engine 812 (analogousto rule AI machine learning system 212 shown in FIG. 2), a ruleanalytics engine 810 (analogous to rule analytics 210 shown in FIG. 2),and an ATDS 806 (analogous to ATDS machine learning system 206 shown inFIG. 2).

Using these inputs, the master AI 814 generates custom rules for eachmatched client using its profile information 802, which includes eachclient's asset profiles (i.e., what computer resources are used by theclient), customer profile (e.g., what type of industry the client isworking in), etc. Further, the profile information 802 includesinformation such as the log source type that is used to report anomaliesin the computer system, a rule design template used to design a rule forthe particular client, predicted thresholds required to trigger a rulefor the client, external threat intelligence describing securitythreats, and escalated offense attributes that describe what attributesof conditions must occur in order for an offense to be escalated to awork ticket, an alert, etc.

As shown in blocks 804, 816, 818, and 820, the master AI 814 is alsoable to generate new rules (blocks 804 and 818), and to generate newoffenses (blocks 816 and 820) in the ticketing system for each customrule generated. That is, the master AI 814 is not only able to createnew rules (by extracting information from profile information 802 usingNLP 801), but is also able to generate a new offense that describes thenew rule being violated.

As shown in FIG. 8, the master AI 814 then notifies security resources822 that a new rule has been generated and/or a new offense hasoccurred. For example, and in an embodiment of the present invention, aclient is notified of the new rule and/or offense, as is the SIEM,ticketing system, incident report platforms (IRP), client reportingsystems, and information portals. The master AI 814 also lets usecase/rule libraries, threat intelligence (TI) sources and vendor systemsknow about the new rule and/or offense.

With reference now to FIG. 9, a high-level flow chart of one or moresteps performed in accordance with one or more embodiments of thepresent invention is presented.

After initiator block 901, one or more processors (e.g., processor 152shown in FIG. 1) inputs a plurality of client profiles to an artificialintelligence (AI) system, as described in block 903. This plurality ofclient profiles is based on an analysis of respective clientenvironments comprising client assets and an intrusion detection alerthistory of a plurality of clients. That is, the client profiles includeinformation such as the client information 802 shown in FIG. 8.

As described in block 905, the processor(s) matching a new clientprofile for a new client to a respective client profile from theplurality of client profiles, where the respective client profile is fora particular client from the plurality of clients. For example, themaster AI system described herein will compare the client information802 (i.e., a client profile) for client C1 shown in FIG. 2 to adifferent client information 802 for client C2 shown in FIG. 2.

In various embodiments of the present invention, the system takesalternative steps to assign a new rule to a client such as client C2.

In one embodiment, and as described in block 907, the processor(s) builda new set of rules for the new client based on a similarity measure of anew client profile to the respective client profile. That is, in thisembodiment, the master AI system will build a rule for client C2 bycomparing client C2 to another client (e.g., client C1). In anembodiment of the present invention, the master AI system will thencreate a rule for client C2 that is a combination of rules currentlyused by client C1.

In another embodiment, and as described in block 909, the processor(s)build a new rule for the new client based a rule used by the particularclient. For example, as shown in table 204, client C2 obtains a new ruleR2′ that is a modified version of rule R2 that is used by client C1.

In another embodiment, and as described in block 911, the processor(s)simply assign a rule from the particular client to the new client basedon the new client profile matching the respective client profile. Thatis, if the profile of client C2 matches the profile of client C1, thenany rule used by client C1 (including rule R2) is assigned for use byclient C2, and vice versa.

In an embodiment of the present invention, the creation/assignment ofrules to the new client (i.e., client C2) is a combination of theprocesses described in blocks 907, 909, and/or 911.

As described in block 913, the processor(s) then receive informationindicating that a violation of the new set of rules has occurred (e.g.,rule R2 has now been violated with regard to the equipment of clientC2).

As described in block 915, the processor(s), in response to the new setof rules being violated, execute a security feature of the computersystem in order to resolve the violation of the new set of rules. Forexample, a firewall may be upgraded, storage devices may be shut down,etc. in order to address the offense (violation of the new set ofrules).

The flow chart ends at terminator block 917.

In an embodiment of the present invention, the processor(s) vector thenew set of rules to create a vectorized rule set, and then test the newset of rules by inputting the vectorized rule set of the new set ofrules in the AI system in order to test the new set of rules against theintrusion detection alert history.

That is, the new set of rules (e.g., rule R2 or rule R2′ shown in FIG.2) is first broken up into a vector of multiple components (actions,events, temporal relationships, affected entities, words, terms, contextrelationships, etc.). For example, the rule “If X happens within fiveminutes of Y happening to Client C, then, perform action Z” can bebroken up into components such as “X,Y” (actions), “happen/happening”(event), “five minutes” (temporal relationship), and “Client C”(affected entity). These components are then displayed in a logicalvector (e.g., in a relational tree) to create a vectorized rule setbased on the new set of rules. That is, vectoring breaks down the ruleset into multiple components, which are then depicted in a relationalgraph such as a tree, which describes the relationship between thedifferent components of the rule set.

This vectorized rule set is then input into an AI system (e.g., the RNN424 shown in FIG. 4), which has been trained to recognize an intrusionbased on an intrusion detection alert history of one or more clients. Assuch, by entering the vectorized rule set into the input layer 403 ofthe RNN 424 shown in FIG. 4, the output layer 407 should (if the newrule set is properly drafted to recognize a particular type ofintrusion) reflect that particular type of intrusion.

In an embodiment of the present invention, the processor(s) applynatural language processing (NLP) to determine a similarity betweenenvironments of the new client and the particular client, and then matchthe new client profile to the respective client profile based on thesimilarity between the environments of the new client and the particularclient.

For example, assume that the features for Client A depicted in theprofile in profile container 508 in FIG. 5 describe a certain ITenvironment (e.g., type-W client computers in a type-X network thatsupports type-Y servers, all of which are protected by a type-Zfirewall). Assume further that Client B described in FIG. 5 also hastype-W client computers in a type-X network that supports type-Yservers, all of which are protected by a type-Z firewall. As such, ifClient A (analogous to client C1 shown in FIG. 2) uses rule R2, theClient B (analogous to client C2 shown in FIG. 2) will be assigned thesame rule R2, or at least a rule (R2′) that is derived from R2.

In an embodiment of the present invention, in which the AI systemdevelops the new set of rules, the new set of rules includes alerts forrespective rules, and the AI system transmits the new set of rules tosecurity system components for the computer system. That is, the AIsystem not only develops the new set of rules (either a direct copy ofan existing rule or a derivation of an existing rule or, alternatively,a completely new rule that is not derived from other rules used by otherclients), but also detects that the new set of rules have been violated,creates the alerts (i.e., offense) that result from the rule(s)violation, and sends the new set of rules to a security system (e.g., afirewall, a security administrator, etc.) for the affected computersystem.

In an embodiment of the present invention, the processor(s) install thenew set of rules into an intrusion detection system. For example, thenew set of rules will be installed on the intrusion detection system 156shown in FIG. 1, which implements the new set of rules and acts on themaccordingly (e.g., in response to signals from the telemetry sources 224shown in FIG. 2) in order to protect the client computers 154.

Thus, one or more of the embodiments of the invention described hereinsignificantly improves security detection coverage for all clientshaving a similar profile (e.g., are in the same industry, in a samegeographical region, etc.). That is, if two clients have a similarprofile, then the security rules they use are harmonized such that theyall use the same (or at least similar) sets of security rules.

The present invention also significantly reduces threat detection timeby use of the ATDS and auto rule generator in the manner describedherein. That is, the ATDS automatically detects security intrusions andthe auto rule generator (e.g., assigning rule R2 to client C2 based onclient C2's similarity to client C1) creates a security detection systemfor client C2 that is more accurate when detecting security issues, andthus reduces the thread detection time.

Thus, the present invention provides a security detection solution thatis unlike the prior art. That is, the prior art does not use machinelearning as described herein to automatically create alerts/offensesbased on a new rule violation (see FIG. 8). Furthermore, the prior artdoes not generate automated rules using NLP methods based on correlatingasset profiles of similar clients (see FIGS. 5-7).

In one or more embodiments, the present invention is implemented usingcloud computing. Nonetheless, it is understood in advance that althoughthis invention includes a detailed description on cloud computing,implementation of the teachings recited herein is not limited to a cloudcomputing environment. Rather, embodiments of the present invention arecapable of being implemented in conjunction with any other type ofcomputing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model includes atleast five characteristics, at least three service models, and at leastfour deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but still is able to specify location at a higherlevel of abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. In one or more embodiments, it is managed by theorganization or a third party and/or exists on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). In one or more embodiments, it is managed by theorganizations or a third party and/or exists on-premises oroff-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 10 illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N communicate with one another.Furthermore, nodes 10 communicate with one another. In one embodiment,these nodes are grouped (not shown) physically or virtually, in one ormore networks, such as Private, Community, Public, or Hybrid clouds asdescribed hereinabove, or a combination thereof. This allows cloudcomputing environment 50 to offer infrastructure, platforms and/orsoftware as services for which a cloud consumer does not need tomaintain resources on a local computing device. It is understood thatthe types of computing devices 54A-54N shown in FIG. 10 are intended tobe illustrative only and that computing nodes 10 and cloud computingenvironment 50 can communicate with any type of computerized device overany type of network and/or network addressable connection (e.g., using aweb browser).

Referring now to FIG. 11, a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 10) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 11 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities that are provided in one or moreembodiments: virtual servers 71; virtual storage 72; virtual networks73, including virtual private networks; virtual applications andoperating systems 74; and virtual clients 75.

In one example, management layer 80 provides the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources comprise application softwarelicenses. Security provides identity verification for cloud consumersand tasks, as well as protection for data and other resources. Userportal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment are utilized in one or more embodiments.Examples of workloads and functions which are provided from this layerinclude: mapping and navigation 91; software development and lifecyclemanagement 92; virtual classroom education delivery 93; data analyticsprocessing 94; transaction processing 95; and security controlprocessing 96, which performs one or more of the features of the presentinvention described herein.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presentinvention. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of various embodiments of the present invention has beenpresented for purposes of illustration and description, but is notintended to be exhaustive or limited to the present invention in theform disclosed. Many modifications and variations will be apparent tothose of ordinary skill in the art without departing from the scope andspirit of the present invention. The embodiment was chosen and describedin order to best explain the principles of the present invention and thepractical application, and to enable others of ordinary skill in the artto understand the present invention for various embodiments with variousmodifications as are suited to the particular use contemplated.

In one or more embodiments of the present invention, any methodsdescribed in the present invention are implemented through the use of aVHDL (VHSIC Hardware Description Language) program and a VHDL chip. VHDLis an exemplary design-entry language for Field Programmable Gate Arrays(FPGAs), Application Specific Integrated Circuits (ASICs), and othersimilar electronic devices. Thus, in one or more embodiments of thepresent invention any software-implemented method described herein isemulated by a hardware-based VHDL program, which is then applied to aVHDL chip, such as a FPGA.

Having thus described embodiments of the present invention of thepresent application in detail and by reference to illustrativeembodiments thereof, it will be apparent that modifications andvariations are possible without departing from the scope of the presentinvention defined in the appended claims.

What is claimed is:
 1. A method comprising: inputting, by one or moreprocessors, a plurality of client profiles to an artificial intelligence(AI) system, wherein the plurality of client profiles are based on ananalysis of respective client environments comprising client assets andan intrusion detection alert history of a plurality of clients;matching, by the one or more processors, a new client profile for a newclient to a respective client profile from the plurality of clientprofiles, wherein the respective client profile is for a particularclient from the plurality of clients; building, by the one or moreprocessors, a new set of rules for the new client based on a similaritymeasure of the new client profile to the respective client profile;receiving, by the one or more processors, information indicating that aviolation of the new set of rules has occurred; and in response to thenew set of rules being violated, executing, by the one or moreprocessors, a security feature of a computer system of the new client inorder to resolve the violation of the new set of rules.
 2. The method ofclaim 1, further comprising: vectoring, by the one or more processors,the new set of rules to create a vectorized rule set; and testing, bythe one or more processors, the new set of rules by inputting thevectorized rule set of the new set of rules in the AI system in order totest the new set of rules against the intrusion detection alert history.3. The method of claim 1, further comprising: applying, by the one ormore processors, natural language processing (NLP) to determine asimilarity between environments of the new client and the particularclient; and matching, by the one or more processors, the new clientprofile to the respective client profile based on the similarity betweenthe environments of the new client and the particular client.
 4. Themethod of claim 1, wherein the AI system develops the new set of rules,wherein the new set of rules includes alerts for respective rules, andwherein the AI system transmits the new set of rules to security systemcomponents for the computer system.
 5. The method of claim 1, furthercomprising: installing, by the one or more processors, the new set ofrules into an intrusion detection system.
 6. A method comprising:inputting, by one or more processors, a plurality of client profiles toan artificial intelligence (AI) system, wherein the plurality of clientprofiles are based on an analysis of respective client environmentscomprising client assets and an intrusion detection alert history of aplurality of clients; matching, by the one or more processors, a newclient profile for a new client to a respective client profile from theplurality of client profiles, wherein the respective client profile isfor a particular client from the plurality of clients; building, by theone or more processors, a new rule for the new client based on a ruleused by the particular client; receiving, by the one or more processors,information indicating that a violation of the new rule has occurred;and in response to the new rule being violated, executing, by the one ormore processors, a security feature of a computer system of the newclient in order to resolve the violation of the new rule.
 7. The methodof claim 6, further comprising: vectoring, by the one or moreprocessors, the new rule to create a vectorized rule; and testing, bythe one or more processors, the new rule by inputting the vectorizedrule in the AI system in order to test the new rule against theintrusion detection alert history.
 8. The method of claim 6, furthercomprising: applying, by the one or more processors, natural languageprocessing (NLP) to determine a similarity between environments of thenew client and the particular client; and matching, by the one or moreprocessors, the new client profile to the respective client profilebased on the similarity between the environments of the new client andthe particular client.
 9. The method of claim 6, wherein the AI systemdevelops the new rule, wherein the new rule includes alerts forrespective rules, and wherein the AI system transmits the new rule tosecurity system components for the computer system.
 10. The method ofclaim 6, further comprising: installing, by the one or more processors,the new rule into an intrusion detection system.
 11. A methodcomprising: inputting, by one or more processors, a plurality of clientprofiles to an artificial intelligence (AI) system, wherein theplurality of client profiles are based on an analysis of respectiveclient environments comprising client assets and an intrusion detectionalert history of a plurality of clients; matching, by the one or moreprocessors, a new client profile for a new client to a respective clientprofile from the plurality of client profiles, wherein the respectiveclient profile is for a particular client from the plurality of clients;assigning, by the one or more processors, a particular rule from theparticular client to the new client based on the new client profilematching the respective client profile; receiving, by the one or moreprocessors, information indicating that a violation of the particularrule has occurred; and in response to the particular rule beingviolated, executing, by the one or more processors, a security featureof a computer system of the new client in order to resolve the violationof the particular rule.
 12. The method of claim 11, further comprising:vectoring, by the one or more processors, the particular rule to createa vectorized rule; and testing, by the one or more processors, theparticular rule by inputting the vectorized rule in the AI system inorder to test the particular rule against the intrusion detection alerthistory.
 13. The method of claim 11, further comprising: applying, bythe one or more processors, natural language processing (NLP) todetermine a similarity between environments of the new client and theparticular client; and matching, by the one or more processors, the newclient profile to the respective client profile based on the similaritybetween the environments of the new client and the particular client.14. The method of claim 11, wherein the AI system develops theparticular rule, wherein the particular rule includes alerts forrespective rules, and wherein the AI system transmits the particularrule to security system components for the computer system.
 15. Themethod of claim 11, further comprising: installing, by the one or moreprocessors, the particular rule into an intrusion detection system. 16.A computer program product comprising a non-transitory computer readablestorage medium having program code embodied therewith, wherein theprogram code is readable and executable by a processor to perform amethod comprising: inputting a plurality of client profiles to anartificial intelligence (AI) system, wherein the plurality of clientprofiles are based on an analysis of respective client environmentscomprising client assets and an intrusion detection alert history of aplurality of clients; matching a new client profile for a new client toa respective client profile from the plurality of client profiles,wherein the respective client profile is for a particular client fromthe plurality of clients; building a new set of rules for the new clientbased on a similarity measure of the new client profile to therespective client profile; receiving information indicating that aviolation of the new set of rules has occurred; and in response to thenew set of rules being violated, executing a security feature of acomputer system of the new client in order to resolve the violation ofthe new set of rules.
 17. The computer program product of claim 16,wherein the method further comprises: vectoring the new set of rules tocreate a vectorized rule set; and testing the new set of rules byinputting the vectorized rule set of the new set of rules in the AIsystem in order to test the new set of rules against the intrusiondetection alert history.
 18. The computer program product of claim 16,wherein the method further comprises: applying natural languageprocessing (NLP) to determine a similarity between environments of thenew client and the particular client; and matching the new clientprofile to the respective client profile based on the similarity betweenthe environments of the new client and the particular client.
 19. Thecomputer program product of claim 16, wherein the AI system develops thenew set of rules, wherein the new set of rules includes alerts forrespective rules, and wherein the AI system transmits the new set ofrules to security system components for the computer system.
 20. Thecomputer program product of claim 16, wherein the program instructionsare provided as a service in a cloud environment.
 21. A computer systemcomprising: one or more processors; one or more computer readablememories; and one or more computer readable non-transitory storagemediums having program instructions stored thereon for execution by atleast one of the one or more processors via at least one of the one ormore computer readable memories, the stored program instructionsexecuted on said at least one of the one or more processors to perform amethod comprising: inputting a plurality of client profiles to anartificial intelligence (AI) system, wherein the plurality of clientprofiles are based on an analysis of respective client environmentscomprising client assets and an intrusion detection alert history of aplurality of clients; matching a new client profile for a new client toa respective client profile from the plurality of client profiles,wherein the respective client profile is for a particular client fromthe plurality of clients; building a new set of rules for the new clientbased on a similarity measure of the new client profile to therespective client profile; receiving information indicating that aviolation of the new set of rules has occurred; and in response to thenew set of rules being violated, executing a security feature of acomputer system of the new client in order to resolve the violation ofthe new set of rules.
 22. The computer system of claim 21, wherein themethod further comprises: vectoring the new set of rules to create avectorized rule set; and testing the new set of rules by inputting thevectorized rule set of the new set of rules in the AI system in order totest the new set of rules against the intrusion detection alert history.23. The computer system of claim 21, wherein the method furthercomprises: applying natural language processing (NLP) to determine asimilarity between environments of the new client and the particularclient; and matching the new client profile to the respective clientprofile based on the similarity between the environments of the newclient and the particular client.
 24. The computer system of claim 21,wherein the AI system develops the new set of rules, wherein the new setof rules includes alerts for respective rules, and wherein the AI systemtransmits the new set of rules to security system components for thecomputer system.
 25. The computer system of claim 21, wherein theprogram instructions are provided as a service in a cloud environment.